In Yosemite, Kings Canyon, and Sequoia National Parks

Home , Rubus armeniacus, Rubus laciniatus

PREDICTING HABITAT SUITABILITY FOR INVASIVE VELVET GRASS

(HOLCUS LANATUS) IN YOSEMITE, KINGS CANYON, AND SEQUOIA

NATIONAL PARKS

Erin Rose Degenstein

A Thesis Presented to

The Faculty of Humboldt State University

In Partial Fulfillment of the Requirements for the Degree

Master of Science in Natural Resources: Environmental and Natural Resource Sciences

Committee Membership

Dr. Alison O’Dowd, Committee Chair

Dr. James Graham, Committee Member

Dr. Michael Mesler, Committee Member

Dr. Yvonne Everett, Natural Resources Program Graduate Coordinator

December 2015

ABSTRACT

PREDICTING HABITAT SUITABILITY OF INVASIVE VELVET GRASS (HOLCUS LANATUS) IN YOSEMITE, KINGS CANYON, AND SEQUOIA NATIONAL PARKS

Erin Rose Degenstein

In order to restore and maintain natural ecosystems in wilderness areas, it is

crucial that land managers understand which ecosystems are most threatened by invasive

plant species and where to search for new infestations. Limited personnel resources and

vast areas of rugged terrain make targeted early detection surveys an urgent need. Habitat

suitability modeling is a spatial analysis tool that provides further understanding and

graphical representation of the potential distribution and spread of invasive plant species.

Velvet grass (Holcus lanatus) is a non-native perennial grass that aggressively invades wet meadows in the Sierra Nevada mountain range of California. This study used 2,865 presence locations of velvet grass in Sequoia, Kings Canyon, and Yosemite National

Parks. Maximum Entropy (Maxent) modeling software was used to develop three

different habitat suitability maps: 1) a 909-meter resolution map based on climate data, 2)

a 10-meter resolution map based on terrain and vegetation features, and 3) a 10-meter

resolution map with terrain and vegetation features, also using elevation data as a

surrogate for climate data. All models were tested for area under the receiver operating

curve and Aikake’s Information Criterion. The vegetation and terrain-based models showed high habitat suitability for un-infested areas of all three national parks. The ii

climate-based model, which could be used to predict habitat suitability in a changing global climate, was limited by its coarse spatial resolution of 909 square meters in an area with heterogeneous topographic features. The 10-meter resolution map using elevation as a surrogate served as a combination between the other two models. All models had high areas under the receiver operating curves, but the high-resolution models using fewer predictor layers had much higher Akaike Information Criterion scores. Separate maps of

Yosemite National Park were also generated to test whether adding soil data improved model performance. Adding soil data improved the area under the receiver operating curve of the Yosemite model, but decreased the Akaike Information Criterion score.

These models reveal characteristics that are common in areas infested by H. lanatus, such

as wetter areas, flatter slopes, and higher temperatures, and highlights un-infested areas

that could be future suitable habitat. Because of the high correlation between elevation

and different climate variables in the three National Parks, the map using elevation as a

surrogate for climate variables provides useful information at a fine enough resolution

(10 meters) that it could be used by managers and field crews to help prioritize early

detection surveys. Additional analysis is needed to determine the impact of potential

vectors (i.e., pack stock, hikers, and rivers) on the location and likelihood of future

infestations.

iii

ACKNOWLEDGEMENTS

Sincere thanks to the faculty, staff, and students at Humboldt State University, including Dr. Alison O’Dowd and Dr. James Graham (Environmental Science and

Management), Dr. Michael Mesler (Botany), Dr. Monica Stephens (Geography),

Humboldt State Institute for Cartographic Design, Humboldt State Institute for Spatial

Analysis, Humboldt State Geospatial Club. Thank you to the Departments of Resource

Management and Science at Yosemite and Kings Canyon/Sequoia National Parks, including Charles Repath, Matt Bahm, Garrett Dickman, Athena Demetry, Rich Thiel,

Ginger Bradshaw, Lizo Meyer, Sylvia Haultain, Laura Pilewski, Jessica Miles, and Corie

Cann.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... iv

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

INTRODUCTION ...... 1

Invasive Plants ...... 1

Invasive Plant Management in Sequoia, Kings Canyon, and Yosemite National Parks ...... 3

Holcus lanatus Background ...... 7

Holcus lanatus in Yosemite, Kings Canyon, and Sequoia National Parks ...... 8

Geographic Information Systems and Invasive Plant Management ...... 9

Research Objective ...... 10

METHODS ...... 11

Data Collection ...... 11

Data Preparation ...... 16

Predictor Layer Selection ...... 16

Yosemite, Kings Canyon, and Sequoia National Park Models ...... 18

Model Parameter Selection ...... 19

Assessing Model Performance ...... 20

RESULTS ...... 22

Summary ...... 22

Model Parameters ...... 22

Model Performance ...... 27 v

Predicted Surfaces ...... 28

Yosemite, Kings Canyon, and Sequoia National Park Models ...... 28

Yosemite Models ...... 42

DISCUSSION ...... 46

Model Evaluation ...... 46

Predictor Layer Selection and Performance ...... 47

Predicted Surfaces ...... 49

CONCLUSIONS AND RECOMMENDATIONS ...... 51

LITERATURE CITED ...... 54

LIST OF TABLES

Table 1. List of actively controlled invasive plant species in Sequoia and Kings Canyon National Parks during Fiscal Year 2013...... 6

Table 2. Descriptions and sources of geospatial data used for this study. SEKI = Sequoia and Kings Canyon National Parks, and YOSE = Yosemite National Park...... 15

Table 3. Predictor layers considered for all models in this study...... 17

Table 4. Pearson's correlation results of elevation and seven BioClim variables from 1,334,428 randomly selected points within the Yosemite, Kings Canyon, and Sequoia National Park boundaries...... 19

Table 5. Summary of AUC and AIC results for each Maxent model...... 27

vii

LIST OF FIGURES

Figure 1. The location of Yosemite, Kings Canyon, and Sequoia National Parks within the state of California with reference to major cities...... 4

Figure 2. This point density map shows the distribution of known H. lanatus presence points in Yosemite National Park. 1,572 of the occurrence points used in this study fell within the boundary of Yosemite National Park. Red (highest) and yellow (lowest) areas represent the density of occurrence points per km2...... 12

Figure 3. Point density map showing locations of H. lanatus known presence points in Kings Canyon and Sequoia National Parks. 1,283 of the presence locations used in this study occurred within the boundaries of Kings Canyon and Sequoia National Parks. Red (highest) and yellow (lowest) areas represent the density of occurrence points per km2. 13

Figure 4. Individual response curves for each BioClim temperature (in degrees Farenheit) variable using different regularization parameter settings of 1, 10, and 20. The logistic output of probability of presence is represented along the vertical axis (scale of 0-1) while the horizontal axis represents predictor variable values...... 24

Figure 5. Individual response curves for each BioClim precipitation (in millimeters) variable using different regularization parameter settings of 1, 10, and 20. The logistic output of probability of presence is represented along the vertical axis (scale of 0-1) while the horizontal axis represents predictor variable values...... 25

Figure 6. Individual response curves for each of the 10-m predictor layer variables using different regularization parameter settings of 1, 10, and 20. The logistic output of probability of presence is represented along the vertical axis (scale of 0-1) while the horizontal axis represents predictor variable values. Vegetation community type data is categorical, with different numbers representing different vegetation community types. 26

Figure 7. Raw mean predicted raster surfaces generated by Maxent for Models A (Climate), B (Vegetation and Terrain), and C (Vegetation and Terrain with Elevation). Values between 0 and 1 represent the log likelihood of suitable habitat for H. lanatus in Yosemite, Kings Canyon, and Sequoia National Parks...... 29

Figure 8. Mean predicted 909-m raster surface for Model A (Climate) in Yosemite, Kings Canyon, and Sequoia National Parks. Values between 0 and 1 represent the log likelihood of suitable habitat for H. lanatus...... 31

Figure 9. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model A (Climate) predictor layers...... 32 viii

Figure 10. Response curves for predictor variables selected for Model A (Climate). The logistic output of probability of presence is represented along the vertical axis while the horizontal axis represents predictor variable values...... 33

Figure 11. Pearson's correlation analysis results of predictor layers used in Model A (Climate). The values in the upper right boxes are Pearson’s correlation values while the boxes in the lower left are the actual pairwise correlation plots for each of the predictor layer variables. All values represent different measurements of either temperature or precipitation...... 34

Figure 12. Mean predicted 10 m raster surface for Model B (Terrain and Vegetation) in Yosemite, Kings Canyon, and Sequoia National Parks. Values between 0 and 1 represent the log likelihood of suitable habitat for H. lanatus...... 36

Figure 13. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model B (Terrain and Vegetation)...... 37

Figure 14. Response curves for predictor variables used in Model B (Terrain and Vegetation): slope, vegetation community type and SAGA wetness index. Vegetation community type is a categorical predictor variable, and different community types are represented by numeric values. The logistic output of probability of presence is represented along the vertical axis while the horizontal axis represents predictor variable values...... 37

Figure 15. Model B (Terrain and Vegetation) Pearson's correlation analysis of predictor layers (slope, vegetation community type, and SAGA wetness index)...... 38

Figure 16. Mean predicted 10 m raster surface for Model C (Terrain, Vegetation, and Elevation) in Yosemite, Kings Canyon, and Sequoia National Parks. Values between 0 and 1 represent the log likelihood of suitable habitat for H. lanatus...... 40

Figure 17. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model C (Terrain, Vegetation, and Elevation)...... 41

Figure 18. Response curves for Model C (Terrain, Vegetation, and Elevation) predictor variables: slope, vegetation community type (categorical, represented by numbers), SAGA wetness index, and elevation...... 42

Figure 19. Mean predicted surfaces for H. lanatus habitat suitability in Yosemite National Park. Values between 0 and 1 represent the log likelihood of suitable habitat...... 42

Figure 20. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Yosemite I Model (Terrain and Vegetation without Soil Type)...... 43 ix

Figure 21. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Yosemite II Model (Terrain and Vegetation with soil type)...... 43

Figure 22. Response curves for Yosemite I Model (Terrain and Vegetation without Soil Type) predictor layers: slope, SAGA wetness index and vegetation community type (categorical, represented by numbers). The logistic output of probability of presence is represented along the vertical axis while the horizontal axis represents predictor layer variable values...... 44

Figure 23. Response curves for Yosemite II Model (Terrain and Vegetation with Soil Type) predictor layers: slope, SAGA wetness index, vegetation community type (categorical) and soil type (categorical). The logistic output of probability of presence is represented along the vertical axis while predictor variables are represented along the horizontal axis...... 44

Figure 24. Pearson's correlation analysis of all predictor layers used in Yosemite models (slope, vegetation community type, SAGA wetness index, and soil type)...... 45

INTRODUCTION

Invasive Plants

The invasion of non-native plants is a widespread concern in the fields of ecology and natural resource management. Non-native invasive plants (hereinafter invasive plants) are species that outcompete and threaten the survival of native plant and animal species (Bossard et al. 2000). Invasive plants may be introduced to an area intentionally or not. Populations can spread quickly via wind, water, human or other animal activity

(Kowarik and von der Lippe 2007, Pyšek et al. 2011). Invasive plants have a wide range of ecosystem impacts. Effects of invasions can be direct or indirect, and include interrupting ecosystem processes and reducing by outcompeting native species biodiversity and productivity (Levine et al. 2003). Invasive plants reduce biodiversity by outcompeting native plants and often replace native plant assemblages with monocultures, reducing food and shelter resources for other species (Randall 2000).

Reduced structural complexity as a result of invasive species monocultures can alter arthropod community composition (Parr et al. 2010, Mitchell 2012). In addition, invasive plant introduction can cause genetic dilution through hybridization with native species

(Huxel 1999). Invasive plants can cause a loss of species or genotypic diversity, which in turn can decrease net primary productivity (Naeem et al. 1996, Crutsinger et al. 2006).

Invasive plants can also alter fire regimes, hydrology, nutrient cycling and erosion processes (Brooks et al. 2004, Dukes and Mooney 2004). Plants that significantly change

the nature or function of an ecosystem are termed “ecosystem transformers” (Rossiter-

Rachor et al. 2009).

The specific interaction between the plant and area of introduction affects the

ecological impact of the invasion. The mechanisms by which a plant invades an

ecosystem may depend on superior access to limiting resources over native vegetation or

a higher resistance to processes that restrict native vegetation (MacDougall and

Turkington 2005). A plant species may be invasive in one ecosystem, but not another and

factors not intrinsic to the plant species or ecosystem may facilitate invasions, such as

disturbance (Lonsdale 1999). Biological and geographic factors, such as seed dispersal

mechanisms and the area of suitable habitat appear likely to govern the invasiveness of a plant and the ecosystem vulnerability to invasion (Lonsdale 1999, Rejmánek 2000).

Areas near human activity or developments can increase both disturbance and the likelihood of the introduction of invasive plants, even in remote backcountry locations

(Morgan and Carnegie 2009). Of documented cases of plant invaders, some plant families are both over- and under-represented. Patterns also suggest that some plants may be more

invasive in ecosystems containing taxa that are less related (Strauss et al. 2006). In

general, infestations tend to occur at lower elevations, in grasslands more than forested

areas, and in areas that have been disturbed by humans or fire (D’Antonio et al. 2004).

However, many invasive plants are expanding their range to higher elevations, which

may be accelerated by global climate change (Dukes and Mooney 1999).

Invasive Plant Management in Sequoia, Kings Canyon, and Yosemite National Parks

Sequoia and Kings Canyon National Parks are located adjacently in the southern

Sierra Nevada in California and Yosemite National Park occupies an area approximately

50 miles northwest of Sequoia and Kings Canyon National Parks (Figure 1). The

National Park Service manages Sequoia and Kings Canyon National Parks as one unit, and Yosemite as a separate unit. Throughout the area of all three parks, elevations range from around 422 meters (1,450 feet) in the western foothills to 4,421 meters (14,505 feet) at the peak of Mount Whitney in Sequoia National Park. The gradient of elevations along the western side of the range contributes to a great diversity in plant communities

(Vankat and Major 1978). The three Parks cover more than 1.6 million acres combined.

The Wilderness Act of 1964 designates over 90% of the area of all three parks as wilderness habitat. Wilderness areas are intended to preserve and protect public lands in their natural state while minimizing human impacts on ecosystem processes (Cole 1996).

The Wilderness Act prohibits roads and the use of mechanized equipment without special permissions. Use of animal pack stock and grazing are still allowed in areas where this occurred before the wilderness designation (McClaran 1989, McCloskey 1995).

Figure 1. The location of Yosemite, Kings Canyon, and Sequoia National Parks within the state of California with reference to major cities.

The invasive plant management program in Sequoia and Kings Canyon National

Park started in 2001 with surveys, GPS mapping, and control of invasive plant populations (Repath 2013). Control methods in the field included hand-removal, securing plastic tarps over infested patches, and herbicide application of different concentrations and surfactants (e.g., glyphosate) (Underwood et al. 2004, Repath 2013). Table 1 lists invasive plant species actively controlled during the 2013 fiscal year in Kings Canyon and Sequoia National Parks. Yosemite national park has a similar program and all three

Parks monitor for and control similar invasive plant species (Underwood et al. 2004,

Repath 2013, Dickman 2014).

Table 1. List of actively controlled invasive plant species in Sequoia and Kings Canyon National Parks during Fiscal Year 2013. Priority for FY13 Field Scientific Name Common Name Season Bromus tectorum Cheatgrass High Carduus pycnocaphalus Italian thistle High Centaurea solstitialis Yellow star thistle High Cirsium vulgare Bull thistle High Digitalis purpurea Foxglove High Holcus lanatus Velvet grass High Phalaris arundinacea Reed canarygrass High Ranunculus testiculatus Bur buttercup High Rubus armeniacus Himalayan blackberry High Rubus laciniatus Cutleaf blackberry High Dactylis glomerata Orchardgrass Med-High Bromus inermis Smooth Brome Med-High Arundo donax Giant reed Med-High Chenopodium album Lamb’s quarters Med-High Convolvulus arvensis Bindweed Med-High Ficus carica Edible fig Med-High Genista monspessulana French broom Med-High Lathyrus latifolius Perennial sweet pea Med-High Marrubium vulgare Horehound Med-High Spartium junceum Spanish broom Med-High Verbascum thapsus Woolly mullein Med-High Vinca major Greater periwinkle Med-High

All three parks consider invasive velvet grass (Holcus lanatus) a high? priority for control because of its threat to remote ecosystems and the difficulty of controlling it

(Underwood et al. 2004, Repath 2013, Dickman 2014). Limited funds require that natural resource managers in the Parks carefully prioritize the actions they take to reduce the negative impacts of invasive plants in wilderness areas. Control of H. lanatus is very costly, labor intensive and often requires a significant time commitment. Thus,

prevention and early detection of new infestations is essential to conserving the

biodiversity of wilderness areas (Rejmánek 2000, Mittermeier et al. 2003).

Holcus lanatus Background

Velvet grass or Holcus lanatus is a non-native perennial bunchgrass that can

quickly displace other species. The plants form dense monocultures with stem heights

from 20 cm to 1 m (Baldwin et al. 2012). The most distinguishable features of H. lanatus

is the velvety hairs covering all aerial parts of the plant. Other features include pink

stripes at the base of the culms, and flowers in panicles of 7 to 15 cm ranging from pale

green to purple (Holloran et al. 2004, Whitson 2006, Baldwin et al. 2012). Holcus lanatus

is successful at excluding other vegetation for many reasons. It has fibrous roots that

form mats in the soil, produces a large amount of litter, is a prolific seed producer, and

contains allelopathic compounds (Newman and Rovira 1975). Holcus lanatus is distributed throughout North America, particularly in meadows and wet places

(Hitchcock 1971). In California, H. lanatus occurs in moist areas, roadsides, meadows

and cultivated fields. It does particularly well in areas under the influence of coastal moisture (Holloran et al. 2004, Gucker 2008, Baldwin et al. 2012).

Likely native to southern Europe and introduced to California in the 18th century,

H. lanatus frequently invades many ecosystems in California and has proven extremely

difficult to control using a variety of methods (Holloran et al. 2004). The California

Invasive Plant Council lists H. lanatus as a “moderate” invasive plant species (Sawyer

and Keeler-Wolf 1995, California Invasive Plant Council 2013). The invasive properties

of H. lanatus cause it to form nearly complete monocultures (Bastow et al. 2008). The

8 invasion of H. lanatus is not of great concern in all places it is present and has even been considered a “new native” by some (Gucker 2008). However, control efforts are ramping up in areas where H. lanatus encroaches on plant communities with a greater conservation value, like backcountry locations of the Sierra Nevada mountain range in

California (Holloran et al. 2004, Underwood et al. 2004).

Holcus lanatus in Yosemite, Kings Canyon, and Sequoia National Parks

In 2006, H. lanatus was identified in wet meadows of the Kern Canyon area in the southeast corner of Sequoia National Park, more than 20 miles away from the nearest road (Repath 2013). Subsequent years included costly removal efforts using a variety of chemical and manual techniques in the Kern Canyon area Sequoia National Park, as well as other smaller infestations (Repath 2013). Infestations of H. lanatus cover hundreds of acres in Yosemite National Park. Field crews are starting to control a recently discovered population 13 miles in the wilderness near Merced Lake, and another population exists in

Pate Valley (Dickman 2014). There has been some success in reducing H. lanatus populations in the Kern Canyon in Sequoia National Park. However, managers have yet to come up with a single most effective solution for control and eradication of populations. Managers and researchers widely agree early detection and rapid response are far more effective tools than the time and resource intensive removal of established stands (Westbrooks 2004).

Geographic Information Systems and Invasive Plant Management

Knowing what to look for and where to look for it makes more efficient use of time, money, and personnel when detecting infestations of invasive plants (Rejmánek

2000). Global Positioning Systems (GPS) make it feasible to collect relatively accurate locations of infestations in the field for analysis purposes and to facilitate relocating sites.

Geographic Information Systems (GIS) offer tools for analysis that integrate spatial relationships with other habitat attributes and allow for complex analysis in a computer-

based environment.

Habitat suitability modeling uses georeferenced ecological data (predictor layers) and known presence locations of a species to predict its potential future distribution.

Maxent is computer modeling software widely used to predict the distributions of invasive species (Phillips and Dudík 2008). Maxent uses presence data and environmental predictor layers to create probability density models (Elith et al. 2011).

While using presence-only data is inherently limited in its ability to predict future

distributions of a species, natural resource scientists regard Maxent as one of the best

methods of species modeling that uses presence-only data (Phillips et al. 2009).

Ecologists widely use Maxent for modeling various plant and animal species (Anderson et al. 2006, Renner et al. 2015).

Research Objective

Natural resource managers in the Sierra Nevada mountain range of California lack the tools to effectively preserve and protect remote wilderness areas from invasive plant species (Rejmánek 2000). Moreover, no model currently exists to identify which areas within Sequoia, Kings Canyon, and Yosemite National Parks are most susceptible to the spread of invasive plants once introduced. The primary objective of this study was to construct a habitat suitability model that predicts the potential future distribution of H. lanatus in Yosemite, Sequoia, and Kings Canyon National Parks with the primary purpose of informing early-detection invasive plant surveys.

METHODS

Data Collection

This study used GPS-collected invasive plant location data collected by National

Park Service (NPS) biological science technicians in Yosemite, Sequoia, and Kings

Canyon National Parks between 1997 and 2014. These data were collected to help

inform invasive plant species control efforts and management decisions in Yosemite,

Sequoia, and Kings Canyon National Parks. Further analysis on the Geodatabase supplied

by the NPS was accomplished by using various GIS and modeling software packages.

Software used in this study included: ArcGIS (ESRI, version 10.1, 2013 and version 10.2,

2014, Redlands, CA), Geographic Resources Analysis Support System (OSGeo, version

6.4.4, 2014), System for Automatic Geoscientific Analyses (version 2.1.4, 2014), Maxent

(Phillips et al. 2007), R (R Foundation for Statistical Computing, version 3.0.2, 2013) and

BlueSpray (Schooner Turtles, ver. 1.1, 2014).

I acquired data for this study primarily from federal government agencies. I acquired occurrence data for H. lanatus from Yosemite National Park (Figure 2) and

Sequoia and Kings Canyon National Parks (Figure 3) (C. Repath, pers. comm., 2013).

I acquired various reference, topographic and biological data that were publicly available and from the NPS Integrated Resource Management Applications (IRMA) website (https://irma.nps.gov/App/) online. The IRMA database contains spatial datasets collected from various different agencies and programs and the U.S. Department of

Agriculture (USDA) Geospatial Gateway website (http://datagateway.nrcs.usda.gov/). I acquired climate data from WorldClim (ww.worldclim.org). Table 2 lists the sources of geospatial data used for this study.

Table 2. Descriptions and sources of geospatial data used for this study. SEKI = Sequoia and Kings Canyon National Parks, and YOSE = Yosemite National Park. Description Park(s) Download Source/Agency Release Original Data Spatial Reference Year Type System (Resolution) Invasive Plant Locations SEKI 2013 National Park 2013 Vector NAD 83 UTM 11N Service (SEKI) Digital Elevation Model SEKI 2013 US Geological 1998 Raster (10 m) NAD 83 UTM 11N Survey Invasive Plant Locations YOSE 2014 National Park 2014 Vector NAD 83 UTM 11N Service (YOSE) National Park Service SEKI, 8/1/14 National Park 2014 Vector GCS NAD 83 Boundaries YOSE Service (IRMA) Digital Elevation Model YOSE 8/5/14 US Geological 2007 Raster (10 m) NAD 83 UTM 11N Survey Trails YOSE 8/5/14 National Park Vector Service (IRMA) Soils YOSE 8/5/14 US Dept. of 5/2006 Vector NAD 83 UTM 11N Agriculture Kings/Sequoia Vegetation SEKI 8/22/2014 National Park 2014 Vector NAD 83 UTM 11N Service Yosemite Vegetation YOSE 8/23/2014 National Park 2007 Vector NAD 83 UTM 11N Service National Hydrography SEKI, 8/27/2014 US Geological 2012 Vector GCS NAD 83 Dataset YOSE Survey WorldClim Interpolated SEKI, 9/27/2014 ww.WorldClim.org Raster (909 m) Climate Layers YOSE (1950-2000) Highways and Major SEKI, 4/11/15 Esri, TomTom Vector GCS WGS 1984 Roads YOSE

Data Preparation

In addition to data I compiled from various sources, I created additional raster

layers with a 10 m spatial resolution from the digital elevation model, including: slope,

aspect, absolute aspect (in ArcMap) and a System for Automated Geoscientific Analysis

(SAGA) wetness index (SAGA GIS). I used stream vector data to generate a raster

containing Euclidean distance to stream in ArcMap. Maxent requires that all predictor

layers used to generate a model be of the same resolution and dimensions. I clipped all 10

m raster layers to the same spatial boundaries where data were available for all layers and projected into North American Datum 1983 Universal Transverse Mercator (UTM) 11

North. I repeated the same procedure for the 909 m BioClim data.

Predictor Layer Selection

I considered several different sets of environmental data for this study (Table 3). I

used Maxent as both an exploratory tool and to create predicted raster surfaces. I used

several combinations of predictor layers to create models for Yosemite, Canyon, and

Sequoia National Parks combined. I ran Pearson's correlations tests in R among all

potential predictor layers to reduce multicollinearity and enhance interpretation of results.

I removed predictor layers that had little contribution to the model or for which I did not

have compelling evidence that they affected habitat suitability reliably. Because soil data

were only available for Yosemite National Park, I ran separate models for just Yosemite

17 both with and without soils data to see if adding a soils predictor layer improved model performance.

Table 3. Predictor layers considered for all models in this study. Layer Source 10-meter Resolution Aspect Derived from 10 m USGS Digital Elevation Model (ArcMap 10.1, ESRI) Absolute Aspect Derived from Aspect layer (360 – absolute value of aspect) Slope Derived from 10 m USGS Digital Elevation Model Elevation USGS Digital Elevation Model Vegetation Community Type Soil Type DRMS –specific Euclidean distance to stream National Hydrography Dataset, Euclidean distance tool (ArcMap 10.1, ESRI) SAGA wetness index Derived from 10 m USGS Digital Elevation Model using SAGA (vers) 909 meter Resolution Annual Mean Temperature WorldClim Mean Diurnal Range WorldClim Isothermality WorldClim Temperature Seasonality WorldClim Max Temperature of Warmest Month WorldClim Minimum Temperature of Coldest WorldClim Month Temperature Annual Range WorldClim Mean Temperature of Wettest Quarter WorldClim Mean Temperature of Driest Quarter WorldClim Mean Temperature of Warmest Quarter WorldClim Mean Temperature of Coldest Quarter WorldClim Annual Precipitation WorldClim Precipitation of Wettest Month WorldClim Precipitation of Driest Month WorldClim Precipitation Seasonality WorldClim Precipitation of Wettest Quarter WorldClim Precipitation of Driest Quarter WorldClim Precipitation of Warmest Quarter WorldClim Precipitation of Coldest Quarter WorldClim

Yosemite, Kings Canyon, and Sequoia National Park Models

Because all predictor layers used in a Maxent model must be of the same

resolution, I considered climate data (909-m resolution) in a separate model from terrain and vegetation community type (10-m resolution). I generated three models encompassing the entire study area (Yosemite, Kings Canyon, and Sequoia National

Parks) in Maxent. For Model A (Climate) I used a series of 909-m resolution raster layers

encompassing Yosemite, Kings Canyon, and Sequoia National Parks. I selected seven of

19 possible predictor layers were from the BioClim dataset based on model contribution

and permutation importance. The final selected layers were: annual mean temperature,

minimum temperature for coldest month, mean temperature of wettest quarter, mean

temperature of coldest quarter, annual precipitation, precipitation of driest month, and

precipitation of driest quarter. I created Model B (Terrain and Vegetation) with a series of

three 10-m resolution raster layers: slope, vegetation community type, and SAGA wetness index. I created Model C (Terrain, Vegetation and Elevation) by adding an elevation predictor layer (digital elevation model) to the predictor layers used in Model

B. I chose elevation due to the 10 m resolution and relatively high correlation between the climate predictor layers used within in the study area, except for annual precipitation, which did not appear to be highly correlated to any other climate variables, either (Table

4). I selected four different predictor layers for Model C: slope, vegetation community

type, SAGA wetness index, and elevation.

Annual mean temperature 0.78 Annual precipitation 0.025 Minimum temperature of coldest month 0.79 Precipitation of driest quarter 0.77 Mean temperature of wettest quarter 0.78 Mean temperature of coldest quarter 0.8 Precipitation of driest month 0.76

Because soil data were only available for Yosemite National Park, I created two

Maxent Yosemite-only models to see if adding a soil type predictor layer positively

affected model performance. For these two models, I used predictor layers and presence

points located only within Yosemite National Park. For Model Yosemite I (Terrain and

Vegetation) I used three 10-m resolution raster layers: slope, vegetation community type, and SAGA wetness index. For Yosemite Model II, I used a 10 m digital elevation model

as a predictor layer in addition to the same three predictor layers used for Model

Yosemite I.

Model Parameter Selection

The “regularization” parameter or beta multiplier in Maxent can be used to smooth out complexities in response curves. The simplified curves help to avoid over-

fitting the model to small sets of presence data beyond what empirical data or common

sense suggest (Phillips et al. 2008). For all models, I adjusted the regularization

parameter to avoid fitting the model too closely to the presence data. For each model, I

ran 100 replicates; 70% of the data were used to train the model, and a random 30% of

the data were set aside as test data. I selected the bootstrap method, which resamples and refits the original data, in Maxent to validate the model. Maxent also used jackknife tests to evaluate individual predictor layers in model performance.

Assessing Model Performance

Habitat suitability models generated by Maxent can be evaluated by model fit and predictive ability (Elith et al. 2011). In Maxent, model fit is measured by what Maxent refers to as “gain.” Prediction refers to the ability of the model to predict independent data. One measure of the overall accuracy of a model’s ability to predict presences contained in the test sample is by the area under the receiver operating characteristic curve (ROC). The ROC curve measures how well the model assigns cases to dichotomous classes by plotting the fraction of true positives (y-axis) against the fraction of false-positives (x-axis) (Fielding and Bell 1997). A diagonal line represents the performance of random/chance prediction, which has a value of 0.5. The higher the area under the curve (AUC), the better a model performs at predicting presences. Maxent

ROC curves compare the fractional predicted area to the omission rate. Because of this, a larger study area in comparison to the range of suitable habitat can result in a higher

AUC. All model results included AUC values calculated by Maxent. Akiake Information

Criterion (AIC) takes into account both goodness of fit and the complexity of a model, and can be used to compare different models that use the same data set (Akaike 1973,

Burnham and Anderson 2002). This provides useful information when determining which model to use in a specific situation. BlueSpray (version A 1.0, 2015) software was used to calculate AIC for all models.

RESULTS

Summary

Results from this study found the presence of H. lanatus in Yosemite, Kings

Canyon, and Sequoia National Parks linked to areas that were wetter (as a result of topography and/or higher precipitation) and areas with warmer temperatures. Areas that received less precipitation during the drier portions of the year were also more likely to contain H. lanatus infestations. The levels and distributions of the likelihood values of habitat suitability varied geographically depending on the type of predictor layer.

Model Parameters

Regularization parameters (beta multipliers) affected the response curves of the predictor layers. All predictor layers were examined using different regularization parameters in Maxent. Figure 4 shows the individual response curves using different regularization parameter settings (i.e., 1, 10, 20). I used a regularization parameter setting of 10 for models in this study. This parameter setting was an effort to reduce over-fitting the model while maintaining the potentially important information contained in the response curves generated (Phillips et al. 2008). A regularization value of 10 removed most of the “rough” edges on the continuous variables without losing too much of the shape of the curves, especially with the BioClim data used in Model A (Figure 4-6 ).

In the response curves shown, y axes represent a logistic output of the raw Maxent model

values between 1 and 10, and x axes represent the values of the predictor layer (Phillips

2006). Because categorical data labels were numerous and their labels not relevant, x axes remain blank.

Model Performance

For all models, the AUC was very high, (i.e., > 0.9). Model C, which was an

attempt to combine elements from Models A and B, had the highest AUC (0.943). For the

Yosemite models, Yosemite II had the highest AUC (0.931). This means that all models

were good and predicting known presence locations compared to the entire study area.

However, it should be noted that AUC values cannot be directly compared to one

another. AIC values, which take into account complexity and can be compared to one

another, differed more. Lower AIC values indicate a better model. For the Yosemite,

Kings Canyon and Sequoia National Parks models, Models B (49606.164) and C

(45935.233) comparatively higher scores than Model A (1903.546). The AIC values for

the Yosemite models were similar to one another, with the one that didn’t use soil data

having a slightly higher value. Table 5 summarizes the AUC and AIC results for all models. Higher AUC values indicate better model fit.

Table 5. Summary of AUC and AIC results for each Maxent model. Model Mean AUC ± standard deviation AIC Yosemite, Kings Canyon, and Sequoia National Parks A 0.932 ± 0.009 1903.546 B 0.914 ± 0.0009 49606.164 C 0.943 ± 0.0001 45935.233 Yosemite National Park Only Yosemite I 0.911 ± 0.003 42716.838 Yosemite II 0.931 ± 0.002 41901.620

Predicted Surfaces

Yosemite, Kings Canyon, and Sequoia National Park Models

The three combinations of predictor layers produced very different results (Figure

7A, B, and C). Model A (Climate) shows increased likelihood of suitable habitat on a large area of the western slopes of the Sierra Nevada mountain range. Model B

(Vegetation and Terrain) shows increased likelihood distributed more equally spread through the parks, concentrated along streams. Model C (Vegetation and Terrain with

Elevation) is the most conservative predicted surface and shows an increased likelihood of habitat suitability primarily along streams on the western slopes of the Sierra Nevada mountain range.

Model A (Climate)

As shown in Figure 8, the output for Model A (Climate) predicted a higher likelihood of suitable habitat mostly on the western portions of the Parks, as well as major drainages. In Yosemite National Park, higher likelihood values were found in the areas along the Tuolumne River and Yosemite Valley in Yosemite. In Kings Canyon, the

Middle Fork Kings River, Kings Canyon, Bubbs Creek, and Paradise Valley all contained higher likelihood habitat suitability values. The western third of Sequoia National Park contained higher likelihood values, as well as the Kern Canyon, where a significant population of H. lanatus already exists.

For Model A (Climate), I narrowed the original 19 BioClim predictor layers to

seven based on percentage permutation importance and percentage contribution to the

model (Figure 9). Four predictor layers were different measurements of temperature, and

three were measurements of precipitation (

Figure 9). The temperature-related predictor layers contributed the most to the model

while precipitation played a larger role in permutation importance. In general, the model

assigned higher likelihood to areas with higher levels of precipitation and warmer

temperatures (Figure 10).

Model A (Climate) 80% 74% 68% 71% 70% 60% 50% 40% 36% 36% 34% 36% 30% 24% 25% 24% 19% 18% 20% 13% 12% 10% 0% Annual Mean Min. Temp. Mean Temp. Mean Temp of Annual Precip. Precip. of Precip. of Temp. Coldest Month of Wettest Coldest Driest Month Driest Quarter Quarter Quarter

Contribution to Model Permutation Importance

Figure 9. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model A (Climate) predictor layers.

According to Pearson’s correlation, annual mean temperature (the largest

contributor to the model) were highly correlated (r ≥ in most cases 0.8) (Figure 11).

Temperature values were all strongly positively correlated with one another. In general, precipitation values were positively correlated with one another. Temperatures and precipitation were negatively correlated.

Model B (Terrain and Vegetation)

The resolution of Model B (Terrain and Vegetation) was 10 meters – much higher than the climate-based Model A and was clearly visible when looking at the raster output

(Figure 12). Model B had higher values of the likelihood of habitat suitability primarily on flat canyon bottoms, and not on steep canyon walls. Overall, the distribution of higher values of likely suitable habitat was more evenly distributed throughout the parks. In contrast to Model A (Climate), Model B (Terrain and Vegetation) predicted lower likelihood values in the western third of Sequoia National Park.

SAGA wetness index was the primary contributor to Model B (Figure 13). The predictor layer response curves for Model B show that the model predicted a higher likelihood of suitable habitat for areas with higher wetness index values, flatter slopes, and certain vegetation community types (Figure 14). Slope values closer to zero had higher likelihoods, and values dropped significantly above slope values of about 40%.

Model B (Terrain and Vegetation) 80% 69.7% 70% 60% 53.2% 50% 40% 26.2% 30% 20.6% 16.9% 20% 13.5% 10% 0% SAGA Wetness Index Slope Vegetation Community Type

Contribution to Model Permutation Importance

Figure 13. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model B (Terrain and Vegetation).

Of the three predictor layers selected for use in Model B (Terrain and Vegetation),

slope and SAGA wetness index showed a slight correlation based on the Pearson’s correlation value (Figure 15). Looking at the variable plots, you can see that areas with higher slope values had lower SAGA wetness index values. The categorical variable

vegetation community type correlated with the other two variables. However, plots show

that certain vegetation types appear limited by high SAGA wetness index values, and

most appeared limited as SAGA wetness index values got close to zero. Slopes above

40% also appeared to be a limiting factor for certain vegetation community types.

Figure 15. Model B (Terrain and Vegetation) Pearson's correlation analysis of predictor layers (slope, vegetation community type, and SAGA wetness index).

Model C (Terrain, Vegetation, and Elevation)

A mean predicted likelihood surface of Model C (Figure 16) shows that it included the much higher 10 meter resolution of Model B (Terrain and Vegetation), with a distribution of higher likelihood values that closely resembles that of Model A

(Climate). However, Model C (Terrain, Vegetation, and Elevation) is more conservative, especially in the steep canyons of the Kern River in Sequoia National Park and Kings

River in Kings Canyon National Park and the Tuolumne and Yosemite valleys in

Yosemite National Park. The rest of the higher likelihood values are concentrated in areas of lower elevations on the western edges of Yosemite and Sequoia National Parks.

Model C (Terrain, Vegetation, and Elevation) was primarliy based on elevation

values. SAGA wetness index and slope values still played slight roles, and vegetation

community type had a negligible effect on the model (Figure 17). The response curve contributed by elevation was far more distinct than those of the other predictor layers, and showed a very narrow region of higher likelihood compared to the full range of elevation values available (Figure 18).

Model C (Terrain, Vegetation, Elevation) 100% 89.8% 80% 72.0% 60% 40% 18.0% 9.8% 7.2% 20% 2.8% 0.3% 0.2% 0% Elevation SAGA Wetness Index Slope Vegetation Community Type

Contribution to Model Permutation Importance

Figure 17. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Model C (Terrain, Vegetation, and Elevation).

Yosemite Models

On a park-wide scale, differences between the predicted surfaces for Yosemite I (Terrain

and Vegetation without Soil Type) and Yosemite II (Terrain and Vegetation with Soil

Type) Models were not very noticeable Figure 19. The model that included soil type as a

predictor variable appeared to have a slightly more conservative output overall.

Figure 19. Mean predicted surfaces for H. lanatus habitat suitability in Yosemite National Park. Values between 0 and 1 represent the log likelihood of suitable habitat.

SAGA wetness index accounted for more than half of the contribution to both

Yosemite models. When soil type was added as a predictor layer to the Yosemite II

model, it contributed more to model fit and permutation importance than slope and

vegetation community type, reducing their impact on the model (Figure 20 and Figure

21). Adding soil type as a predictor layer only slightly altered the response curves of the other predictor layer variables (Figure 22 and Figure 23). None of the predictor layers used in the Yosemite models were highly correlated with one another (Figure 24).

Yosemite I 80% 71.4% 62.3% 60% 40% 29.1% 21.1% 20% 7.5% 8.6% 0% SAGA Wetness Index Slope Vegetation Community Type

Contribution to Model Permutation Importance

Figure 20. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Yosemite I Model (Terrain and Vegetation without Soil Type).

Yosemite II 80% 58.9% 60% 47.3% 33.6% 40% 22.0% 20% 11.9% 10.9% 7.3% 8.3% 0% SAGA Wetness Index Soil Type Vegetation Community Slope Type

Contribution to Model Permutation Importance

Figure 21. Percentage contribution and percentage permutation importance for individual predictor layers selected to construct Yosemite II Model (Terrain and Vegetation with soil type).

Figure 24. Pearson's correlation analysis of all predictor layers used in Yosemite models (slope, vegetation community type, SAGA wetness index, and soil type).

DISCUSSION

Model Evaluation

Though the distribution of likelihood values across predicted surfaces varied

drastically between model outputs, all models had very high AUC values (>0.9, Table 5).

While commonly used as a standard for measuring model performance, AUC values are

not a good standalone measure because AUC tends to be higher for species with a narrow

range relative to the study area (Lobo et al. 2008). Furthermore, using presence-only data limits the evaluation of model performance because the model is evaluated by treating

background points as pseudo-absences, which may not be an accurate representation.

Despite the limitations mentioned above, AUC values may still be used to compare similar models to one another, such as the models in this study, to see if adding or removing different predictor layers affects model fit (Lobo et al. 2008). For example,

Model C (Terrain, Vegetation and Elevation), which was an attempt to combine the very different characteristics of Model A (Climate) and Model B (Terrain and Vegetation), had the highest AUC (0.943) of the three models. The higher AUC for Model C (Terrain,

Vegetation and Elevation) was a result of the model being more closely fitted to the presence data. Yosemite Model II also increased the fit of the model to the presence data by adding soil type as a predictor, resulting in an increase of AUC from 0.911 to 0.931 over Yosemite Model I.

While the only model that showed a much lower value than similar models was

Model A (Climate), other factors, such as applicability, should be considered before considering it the “best” model. A 10-m resolution model may still be preferable to use for field surveys.

Predictor Layer Selection and Performance

For the high-resolution 10-m models, I initially considered Euclidean distance to stream as a potential predictor layer, but later rejected it in favor of an elevation-derived topographic wetness index for two reasons. First, literature, existing presence data, as well as personal field observations show that H. lanatus is found in wetter areas

(Holloran et al. 2004, Gucker 2008, Baldwin et al. 2012), but proximity to a stream is not always an accurate indicator of soil wetness, especially in rugged terrain where a short horizontal distance may include a drastic increase in elevation (i.e. steep canyon walls).

Second, H. lanatus populations are frequently found along river banks, especially where flow decreases, which may indicate that rivers themselves are dispersing H. lanatus seeds rather than affecting the habitat suitability. There are many variations of calculations for topographic wetness indices, and SAGA wetness index was selected because it uses a more realistic multiple flow direction method rather than assuming all water travels in a thin sheet (Freeman 1991, Böhner et al. 2001, Böhner and Selige 2006, Poff et al. 2010).

Because different plant communities will show different levels of susceptibility or resilience to invasion, thus affecting the habitat suitability, categorical vegetation

community type data was included in the high resolution models, even though it did not

contribute as much as the other predictor layers. Furthermore, some vegetation categories

such as exposed rock could help further exclude unsuitable habitat. However, the large

number of categories compared to the relatively small sample of presence locations over

the total area affected the ability of this categorical data to add very much predictive

power to the models.

Slope and SAGA wetness index are related, in that SAGA wetness index uses slope

calculations to come up with index values. However, slope values were still included as a

predictor layer in the 10-meter resolution models because it still represents a separate

physical characteristic that effects habitat suitability, including gravitational pull (Hirzel

et al. 2006).

Using BioClim data for Model A is advantageous because future suitability can be

forecasted using various existing climate change models (Hijmans and Graham 2006).

However, the spatial resolution of Model A was limited in its practical application for field crews, as habitat characteristics can vary drastically within any given 909 m pixel.

Because BioClim data are interpolated from multiple weather stations, the higher the spatial resolution of the data, the less accurate the predicted values are likely to be (Booth et al. 2014).

The 10-m models (B and C) provided much more detailed displays, but caution must be exercised and uncertainty must be taken into account when interpreting the results.

Uncertainty is present in all data collected, and include user error and accuracy of devices

used to measure both presence locations and the various environmental data collected

(Molenaar et al. 2005, Wang et al. 2005). While Models B and C provided more useful

geographic information pertaining to likelihood of habitat suitability for the field crew

than the 909-m resolution of Model C, their actual accuracy is less than 10 meters.

In Model C (Terrain, Vegetation, and Elevation) elevation was only used as a

surrogate for higher resolution climate data and only works within the context of the

study area. Elevation itself is not the limiting factor in H. lanatus distribution worldwide,

but the climatic conditions associated with elevation are more likely to determine whether

the plant species will thrive.

Predicted Surfaces

Model A (Climate) is based only on precipitation and temperature values, which

are heavily correlated with elevation, therefore, it did not find the eastern portions of the

Sierra Nevada within Yosemite, Kings Canyon, or Sequoia National Parks to be suitable

habitat for H. lanatus. This would be good news for managers, as it reduces the total area of potential infestation by H. lanatus, especially in some of the most remote areas of the

Parks. However, targeting specific locations to search would be difficult with the relatively lower spatial resolution of 909-meter pixels. Model B (Terrain and Vegetation)

provides higher resolution, but predicts that likely suitable habitat is distributed much

more evenly throughout the study area, which would result in much more time and

resources needed for early detection surveys. The predicted surface for Model C

(Terrain, Vegetation and Elevation) combines elements of the previous two models.

Model C predicts that H. lanatus distribution will be constrained to the flatter slopes and lower elevations of the western portions of the Sierra Nevada throughout the Parks, especially on the flat bottoms of canyons. Because the model output is so highly dependent on climate/elevation, it would be useful to know more about how much of a limiting factor climate and/or elevation are to habitat suitability for H. lanatus.

The Yosemite models generated to test the effect of adding a categorical soil type predictor layer created very similar predicted surfaces. Yosemite II, the model using soils, was slightly more conservative in its predictions. Because categorical soil type data constrained the likelihood of suitable habitat in Yosemite National Park, soil may play a role in determining habitat suitability in that Park. However, it could also be a consequence of a small sample of presence points relative to the study area.

CONCLUSIONS AND RECOMMENDATIONS

Maps generated from these predicted models can help National Park Service personnel in early detection of H. lanatus in Yosemite, Kings Canyon, and Sequoia

National Parks. Managers can use these models to prioritize field surveys based on higher likelihood values of habitat suitability for H. lanatus. Managers could use these models

(particularly when used in digital form and referenced to other Park features) to compare areas potentially suitable to H. lanatus with respect to known endangered species or critical habitats. Field crews and other park staff such as wilderness rangers could use printed maps to assist them in locating areas of higher H. lanatus suitability that aren’t frequently surveyed. Because the maps are high-resolution, the optimal use by field crews would be in digital format on a mobile electronic device.

While the models produced in this study may be helpful in determining priorities for early detection surveys, they present only a partial picture of potential future distributions of H. lanatus in Yosemite, Kings Canyon, and Sequoia National Parks.

Presence-only data, as used in this study, have many limitations and uncertainties, including spatial bias towards areas that are more frequently surveyed (i.e. near trails, roads, and ranger stations) whereas the background points (pseudo-absences) are randomly generated from the entire study area. Adding the same bias to background points has shown to improve model performance (Phillips et al. 2009).

Even if reliable absence data were used in a model, it would not necessarily indicate unsuitable habitat. Further research into the presence of H. lanatus locations, along with

laboratory testing could help determine which factors are truly the most important in

suitable habitat for H. lanatus. Adding more presence data from outside the Parks would

improve the ability to create a model that could be more generalizable. Because H. lanatus is such a prolific seeder and presence appears to be related to soil moisture

(Beddows 1961, Baldwin et al. 2012), laboratory tests on soil saturation and seed viability could help determine the range of soil wetness in which H. lanatus can germinate. A study in Europe has shown that H. lanatus was more adapted to climatic factors than soil factors on a local scale, and similar studies could perhaps help determine the relative importance of these factors in the Sierra Nevada (Macel et al. 2007)

Pack stock use has long been suspected to impact the presence and spread of H. lanatus invasions. In Sequoia National Park, H. lanatus invasions have influenced grazing regulations, preventing stock users from grazing in certain meadows when entering the Park from adjacent lands with dense infestations (Repath 2013). In the Kern

Canyon in the southern part of Sequoia National Park, all models predict higher likelihood of suitable habitat throughout the lower 10 miles of the canyon to the Kern

Ranger station, but populations lie distinctly near Upper Funston and Lower Funston meadows, 4 miles apart respectively, which have both been historically used for grazing pack stock.

Habitat suitability is just one element affecting the likelihood of invasive plant species occurrence. Risk of introduction through natural and anthropogenic factors should also be considered when trying to determine likelihood of presence. In addition to

53 being associated with various climatic and topographic features, invasive plant populations are often found in proximity to places where there are more people or vehicles, such as roads (Gelbard and Belnap 2003). Future modeling efforts could incorporate a predicted habitat suitability surface into a risk of introduction model. High- resolution predicted surface outputs could be very useful to field personnel conducting early detection surveys.

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